Organizations are not dealing with artificial intelligence risks as they should. See how to recognize the most common.
As with everything relatively new in business, we also begin to see potential dangers as we discover many benefits and value. And as far as the risks of artificial intelligence are concerned, they can lead to significant consequences.
For individuals, we refer to risks such as physical and financial integrity; for organizations, to things like business performance, compliance and reputation; and in society, national security, and political and economic stability.
Now, like everything in an embryonic stage, McKinsey says, organizations either underestimate these risks or overestimate their ability to mitigate them — through voices like Stephen Hawking, Elon Musk, and Bill Gates have already warned of the fallibility of this thinking. According to the consultancy, although they want to remove embarrassment as something that does not concern them, the current scenario in companies is as follows:
Furthermore, they are unsure what AI risks might appear to them: Deloitte’s State of AI in the enterprise shows that less than 1/3 of organizations engage in more than three AI risk management activities. Fewer than four in 10 early adopters say they are fully prepared to deal with the potential risks.
PwC reports similar data, with only 10% of respondents to its survey saying they are entirely confident about their organization’s AI risk management.
To change this scenario, it is necessary to understand better the types of AI risk organizations deal with, their interdependencies, and their causes. It is these risks of artificial intelligence that we will talk about in this article.
Why Deal With The Risks Of Artificial Intelligence
Suppose studies show, as we saw above, that organizations are turning a blind eye to artificial intelligence risks or acting only to mitigate explicitly regulated risks. In that case, Deloitte’s State of AI in the Enterprise 2020 shows why they shouldn’t. Do.
According to the consultancy, actively managing the risks of AI reduces the challenges of adopting the technology and, therefore, leads to more significant competitive advantages. See the survey data:
- While 41% of companies that engage in more than three AI risk management activities are slowing AI projects down because of these risks, 58% of organizations that do not manage risk are doing the same.
- 46% of organizations dealing with risk say that AI has given them significant competitive advantages, while only 20% of those don’t see this benefit.
According to Deloitte, as the organization matures in its skills and scales its AI projects, its concern for related risks also grows. For those with medium maturity, this means an increase in worry – which leads them to develop processes, behaviors, or skills to decrease it. In the long term, this increase in controls causes the concern of organizations with AI maturity to fall when they become sufficiently mature.
The Risks Of Artificial Intelligence
Data
We have already talked about data quality here on the blog and the main challenges in artificial intelligence projects, and we reiterate. With the increase in unstructured data being captured from the internet, it is easier to use sensitive information or find it inadvertently. We are hidden in anonymized data.
Not only that, but using incomplete, biased, and uneven data makes it even more challenging to find the root cause of unintended biases within a data set.
Therefore, using personal data without consent is one of the most common risks associated with AI. Data management and governance practices are important actions to avoid risks related to data quality.
Security
It is not uncommon for fraudsters to exploit data that feeds AI systems or that software flaws and vulnerabilities can considerably impact the performance of AI systems.
Related to this point, we can still mention the changes and new rules in the regulatory landscape, which can impact projects in progress, for example.
Algorithms
The models and algorithms can create problems when they deliver biased results and when their decisions and actions are not sufficiently explainable and transparent, as is the case with the black box.
The high degree of bias of a seemingly simple algorithm to identify a person’s gender by their name or email address, in the case of Generify, illustrates the problem well. So what about complex models that even well-trained data scientists struggle to understand precisely how their algorithms make decisions?
Knowing how to explain and justify the decision process of an AI is fundamental, and feeding it with data in sufficient quantity, variety, and quality will be essential to avoid failures that affect operations, bad decisions based on AI recommendations, etc.
Humans
The interface between humans and machines is also an area of risk. Intentionally or unintentionally, users can contribute to adding bias in AI by building, analyzing outputs, and applying solutions based on insights from AI-powered systems.
The ability to avoid these risks is directly associated with different perspectives within the technical and business teams. Only then will a plurality of insights come to light.
There is also the potential risk of adverse reactions from employees to using AI, caused by fears about the loss of work due to the automation provided by the technology.
Artificial Intelligence Risks: Turn Worried Into Preparedness
We have seen that AI is not exempt from potential and current risks and that most organizations do not feel prepared to deal with them. Being a rapidly developing field, it is natural that the level of security uncertainty is high.
Still, creating AI-powered tools and systems must remain a top priority for organizations. Because, in this case, the benefits of well-built AI projects far outweigh the risks.
Also Read: Why the industry is investing in Artificial Intelligence
